---
tags:
- cytology foundation model
- vision transformer
- cervical screening
license: apache-2.0
---
## How to use UniCAS to extract features.

The code below can be used to run inference; `UniCAS` expects images of size 224x224 that were extracted at 20× magnification.

```python
import functools
import timm
import torch
from torchvision import transforms

params = {
    'patch_size': 16, 
    'embed_dim': 1024, 
    'depth': 24, 
    'num_heads': 16, 
    'init_values': 1e-05, 
    'mlp_ratio': 2.671875 * 2, 
    'mlp_layer': functools.partial(
        timm.layers.mlp.GluMlp, gate_last=False
    ), 
    'act_layer': torch.nn.modules.activation.SiLU, 
    'no_embed_class': False, 
    'img_size': 224, 
    'num_classes': 0, 
    'in_chans': 3
}

model = timm.models.VisionTransformer(**params)
print(model.load_state_dict(torch.load("UniCAS.pth"), strict=False))
model = model.eval().to("cuda")


transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize(
        mean=(0.485, 0.456, 0.406),
        std=(0.229, 0.224, 0.225),
    ),
])

input = torch.rand(3, 224, 224)
input = transforms.ToPILImage()(input)
input = transform(input).unsqueeze(0)
with torch.no_grad():
    features = model(input.to("cuda"))
print(features.shape)  # torch.Size([1, 1024])
```